{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Implementing a Neural Network\n",
    "In this exercise we will develop a neural network with fully-connected layers to perform classification, and test it out on the CIFAR-10 dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# A bit of setup\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from cs231n.classifiers.neural_net import TwoLayerNet\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "    \"\"\" returns relative error \"\"\"\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "We will use the class `TwoLayerNet` in the file `cs231n/classifiers/neural_net.py` to represent instances of our network. The network parameters are stored in the instance variable `self.params` where keys are string parameter names and values are numpy arrays. Below, we initialize toy data and a toy model that we will use to develop your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# Create a small net and some toy data to check your implementations.\n",
    "# Note that we set the random seed for repeatable experiments.\n",
    "\n",
    "input_size = 4\n",
    "hidden_size = 10\n",
    "num_classes = 3\n",
    "num_inputs = 5\n",
    "\n",
    "def init_toy_model():\n",
    "    np.random.seed(0)\n",
    "    return TwoLayerNet(input_size, hidden_size, num_classes, std=1e-1)\n",
    "\n",
    "def init_toy_data():\n",
    "    np.random.seed(1)\n",
    "    X = 10 * np.random.randn(num_inputs, input_size)\n",
    "    y = np.array([0, 1, 2, 2, 1])\n",
    "    return X, y\n",
    "\n",
    "net = init_toy_model()\n",
    "X, y = init_toy_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forward pass: compute scores\n",
    "Open the file `cs231n/classifiers/neural_net.py` and look at the method `TwoLayerNet.loss`. This function is very similar to the loss functions you have written for the SVM and Softmax exercises: It takes the data and weights and computes the class scores, the loss, and the gradients on the parameters. \n",
    "\n",
    "Implement the first part of the forward pass which uses the weights and biases to compute the scores for all inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your scores:\n",
      "[[-0.81233741 -1.27654624 -0.70335995]\n",
      " [-0.17129677 -1.18803311 -0.47310444]\n",
      " [-0.51590475 -1.01354314 -0.8504215 ]\n",
      " [-0.15419291 -0.48629638 -0.52901952]\n",
      " [-0.00618733 -0.12435261 -0.15226949]]\n",
      "\n",
      "correct scores:\n",
      "[[-0.81233741 -1.27654624 -0.70335995]\n",
      " [-0.17129677 -1.18803311 -0.47310444]\n",
      " [-0.51590475 -1.01354314 -0.8504215 ]\n",
      " [-0.15419291 -0.48629638 -0.52901952]\n",
      " [-0.00618733 -0.12435261 -0.15226949]]\n",
      "\n",
      "Difference between your scores and correct scores:\n",
      "3.6802720745909845e-08\n"
     ]
    }
   ],
   "source": [
    "scores = net.loss(X)\n",
    "print('Your scores:')\n",
    "print(scores)\n",
    "print()\n",
    "print('correct scores:')\n",
    "correct_scores = np.asarray([\n",
    "  [-0.81233741, -1.27654624, -0.70335995],\n",
    "  [-0.17129677, -1.18803311, -0.47310444],\n",
    "  [-0.51590475, -1.01354314, -0.8504215 ],\n",
    "  [-0.15419291, -0.48629638, -0.52901952],\n",
    "  [-0.00618733, -0.12435261, -0.15226949]])\n",
    "print(correct_scores)\n",
    "print()\n",
    "\n",
    "# The difference should be very small. We get < 1e-7\n",
    "print('Difference between your scores and correct scores:')\n",
    "print(np.sum(np.abs(scores - correct_scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forward pass: compute loss\n",
    "In the same function, implement the second part that computes the data and regularization loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference between your loss and correct loss:\n",
      "1.7985612998927536e-13\n"
     ]
    }
   ],
   "source": [
    "loss, _ = net.loss(X, y, reg=0.05)\n",
    "correct_loss = 1.30378789133\n",
    "\n",
    "# should be very small, we get < 1e-12\n",
    "print('Difference between your loss and correct loss:')\n",
    "print(np.sum(np.abs(loss - correct_loss)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Backward pass\n",
    "Implement the rest of the function. This will compute the gradient of the loss with respect to the variables `W1`, `b1`, `W2`, and `b2`. Now that you (hopefully!) have a correctly implemented forward pass, you can debug your backward pass using a numeric gradient check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W2 max relative error: 3.440708e-09\n",
      "b2 max relative error: 4.447625e-11\n",
      "W1 max relative error: 3.561318e-09\n",
      "b1 max relative error: 2.738421e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.gradient_check import eval_numerical_gradient\n",
    "\n",
    "# Use numeric gradient checking to check your implementation of the backward pass.\n",
    "# If your implementation is correct, the difference between the numeric and\n",
    "# analytic gradients should be less than 1e-8 for each of W1, W2, b1, and b2.\n",
    "\n",
    "loss, grads = net.loss(X, y, reg=0.05)\n",
    "\n",
    "# these should all be less than 1e-8 or so\n",
    "for param_name in grads:\n",
    "    f = lambda W: net.loss(X, y, reg=0.05)[0]\n",
    "    param_grad_num = eval_numerical_gradient(f, net.params[param_name], verbose=False)\n",
    "    print('%s max relative error: %e' % (param_name, rel_error(param_grad_num, grads[param_name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train the network\n",
    "To train the network we will use stochastic gradient descent (SGD), similar to the SVM and Softmax classifiers. Look at the function `TwoLayerNet.train` and fill in the missing sections to implement the training procedure. This should be very similar to the training procedure you used for the SVM and Softmax classifiers. You will also have to implement `TwoLayerNet.predict`, as the training process periodically performs prediction to keep track of accuracy over time while the network trains.\n",
    "\n",
    "Once you have implemented the method, run the code below to train a two-layer network on toy data. You should achieve a training loss less than 0.02."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final training loss:  0.017149607938732093\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "net = init_toy_model()\n",
    "stats = net.train(X, y, X, y,\n",
    "            learning_rate=1e-1, reg=5e-6,\n",
    "            num_iters=100, verbose=False)\n",
    "\n",
    "print('Final training loss: ', stats['loss_history'][-1])\n",
    "\n",
    "# plot the loss history\n",
    "plt.plot(stats['loss_history'])\n",
    "plt.xlabel('iteration')\n",
    "plt.ylabel('training loss')\n",
    "plt.title('Training Loss history')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the data\n",
    "Now that you have implemented a two-layer network that passes gradient checks and works on toy data, it's time to load up our favorite CIFAR-10 data so we can use it to train a classifier on a real dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 3072)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 3072)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "from cs231n.data_utils import load_CIFAR10\n",
    "\n",
    "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):\n",
    "    \"\"\"\n",
    "    Load the CIFAR-10 dataset from disk and perform preprocessing to prepare\n",
    "    it for the two-layer neural net classifier. These are the same steps as\n",
    "    we used for the SVM, but condensed to a single function.  \n",
    "    \"\"\"\n",
    "    # Load the raw CIFAR-10 data\n",
    "    cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "    \n",
    "    # Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "    try:\n",
    "       del X_train, y_train\n",
    "       del X_test, y_test\n",
    "       print('Clear previously loaded data.')\n",
    "    except:\n",
    "       pass\n",
    "\n",
    "    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "        \n",
    "    # Subsample the data\n",
    "    mask = list(range(num_training, num_training + num_validation))\n",
    "    X_val = X_train[mask]\n",
    "    y_val = y_train[mask]\n",
    "    mask = list(range(num_training))\n",
    "    X_train = X_train[mask]\n",
    "    y_train = y_train[mask]\n",
    "    mask = list(range(num_test))\n",
    "    X_test = X_test[mask]\n",
    "    y_test = y_test[mask]\n",
    "\n",
    "    # Normalize the data: subtract the mean image\n",
    "    mean_image = np.mean(X_train, axis=0)\n",
    "    X_train -= mean_image\n",
    "    X_val -= mean_image\n",
    "    X_test -= mean_image\n",
    "\n",
    "    # Reshape data to rows\n",
    "    X_train = X_train.reshape(num_training, -1)\n",
    "    X_val = X_val.reshape(num_validation, -1)\n",
    "    X_test = X_test.reshape(num_test, -1)\n",
    "\n",
    "    return X_train, y_train, X_val, y_val, X_test, y_test\n",
    "\n",
    "\n",
    "# Invoke the above function to get our data.\n",
    "X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train a network\n",
    "To train our network we will use SGD. In addition, we will adjust the learning rate with an exponential learning rate schedule as optimization proceeds; after each epoch, we will reduce the learning rate by multiplying it by a decay rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1000: loss 2.302970\n",
      "iteration 100 / 1000: loss 2.302474\n",
      "iteration 200 / 1000: loss 2.297076\n",
      "iteration 300 / 1000: loss 2.257328\n",
      "iteration 400 / 1000: loss 2.230484\n",
      "iteration 500 / 1000: loss 2.150620\n",
      "iteration 600 / 1000: loss 2.080736\n",
      "iteration 700 / 1000: loss 2.054914\n",
      "iteration 800 / 1000: loss 1.979290\n",
      "iteration 900 / 1000: loss 2.039101\n",
      "Validation accuracy:  0.287\n"
     ]
    }
   ],
   "source": [
    "input_size = 32 * 32 * 3\n",
    "hidden_size = 50\n",
    "num_classes = 10\n",
    "net = TwoLayerNet(input_size, hidden_size, num_classes)\n",
    "\n",
    "# Train the network\n",
    "stats = net.train(X_train, y_train, X_val, y_val,\n",
    "            num_iters=1000, batch_size=200,\n",
    "            learning_rate=1e-4, learning_rate_decay=0.95,\n",
    "            reg=0.25, verbose=True)\n",
    "\n",
    "# Predict on the validation set\n",
    "val_acc = (net.predict(X_val) == y_val).mean()\n",
    "print('Validation accuracy: ', val_acc)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Debug the training\n",
    "With the default parameters we provided above, you should get a validation accuracy of about 0.29 on the validation set. This isn't very good.\n",
    "\n",
    "One strategy for getting insight into what's wrong is to plot the loss function and the accuracies on the training and validation sets during optimization.\n",
    "\n",
    "Another strategy is to visualize the weights that were learned in the first layer of the network. In most neural networks trained on visual data, the first layer weights typically show some visible structure when visualized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the loss function and train / validation accuracies\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(stats['loss_history'])\n",
    "plt.title('Loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Loss')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(stats['train_acc_history'], label='train')\n",
    "plt.plot(stats['val_acc_history'], label='val')\n",
    "plt.title('Classification accuracy history')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Classification accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from cs231n.vis_utils import visualize_grid\n",
    "\n",
    "# Visualize the weights of the network\n",
    "\n",
    "def show_net_weights(net):\n",
    "    W1 = net.params['W1']\n",
    "    W1 = W1.reshape(32, 32, 3, -1).transpose(3, 0, 1, 2)\n",
    "    plt.imshow(visualize_grid(W1, padding=3).astype('uint8'))\n",
    "    plt.gca().axis('off')\n",
    "    plt.show()\n",
    "\n",
    "show_net_weights(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tune your hyperparameters\n",
    "\n",
    "**What's wrong?**. Looking at the visualizations above, we see that the loss is decreasing more or less linearly, which seems to suggest that the learning rate may be too low. Moreover, there is no gap between the training and validation accuracy, suggesting that the model we used has low capacity, and that we should increase its size. On the other hand, with a very large model we would expect to see more overfitting, which would manifest itself as a very large gap between the training and validation accuracy.\n",
    "\n",
    "**Tuning**. Tuning the hyperparameters and developing intuition for how they affect the final performance is a large part of using Neural Networks, so we want you to get a lot of practice. Below, you should experiment with different values of the various hyperparameters, including hidden layer size, learning rate, numer of training epochs, and regularization strength. You might also consider tuning the learning rate decay, but you should be able to get good performance using the default value.\n",
    "\n",
    "**Approximate results**. You should be aim to achieve a classification accuracy of greater than 48% on the validation set. Our best network gets over 52% on the validation set.\n",
    "\n",
    "**Experiment**: You goal in this exercise is to get as good of a result on CIFAR-10 as you can (52% could serve as a reference), with a fully-connected Neural Network. Feel free implement your own techniques (e.g. PCA to reduce dimensionality, or adding dropout, or adding features to the solver, etc.)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Explain your hyperparameter tuning process below.**\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ 因为现在应该处于欠拟合的状态，我肯定会先从batch_size、learning_rate、hidden_size这3个角度先开始让它达到过拟合的状态，然后调整reg、使用dropout来增加泛化能力，使在验证集上的准确率上升，最好这些固定了，再试试这些参数在PCA降维的图上有没有提升！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr = 0.000010, bs = 128, rs = 0.150000, hs = 50,\n",
      "iteration 0 / 2000: loss 2.302808\n",
      "iteration 500 / 2000: loss 2.302612\n",
      "iteration 1000 / 2000: loss 2.302268\n",
      "iteration 1500 / 2000: loss 2.301270\n",
      "val_acc = 0.165000\n",
      "==============================================================\n",
      "lr = 0.000010, bs = 128, rs = 0.150000, hs = 100,\n",
      "iteration 0 / 2000: loss 2.303070\n",
      "iteration 500 / 2000: loss 2.302798\n",
      "iteration 1000 / 2000: loss 2.302252\n",
      "iteration 1500 / 2000: loss 2.300093\n",
      "val_acc = 0.191000\n",
      "==============================================================\n",
      "lr = 0.000010, bs = 128, rs = 0.150000, hs = 150,\n",
      "iteration 0 / 2000: loss 2.303294\n",
      "iteration 500 / 2000: loss 2.302852\n",
      "iteration 1000 / 2000: loss 2.302073\n",
      "iteration 1500 / 2000: loss 2.298181\n",
      "val_acc = 0.183000\n",
      "==============================================================\n",
      "lr = 0.000010, bs = 128, rs = 0.150000, hs = 200,\n",
      "iteration 0 / 2000: loss 2.303556\n",
      "iteration 500 / 2000: loss 2.303029\n",
      "iteration 1000 / 2000: loss 2.301558\n",
      "iteration 1500 / 2000: loss 2.297595\n",
      "val_acc = 0.201000\n",
      "==============================================================\n",
      "lr = 0.000010, bs = 128, rs = 0.300000, hs = 50,\n",
      "iteration 0 / 2000: loss 2.303048\n",
      "iteration 500 / 2000: loss 2.302937\n",
      "iteration 1000 / 2000: loss 2.302412\n",
      "iteration 1500 / 2000: loss 2.301015\n",
      "val_acc = 0.188000\n",
      "==============================================================\n",
      "lr = 0.000010, bs = 128, rs = 0.300000, hs = 100,\n",
      "iteration 0 / 2000: loss 2.303520\n",
      "iteration 500 / 2000: loss 2.303380\n",
      "iteration 1000 / 2000: loss 2.302630\n",
      "iteration 1500 / 2000: loss 2.300275\n",
      "val_acc = 0.175000\n",
      "==============================================================\n",
      "lr = 0.000010, bs = 128, rs = 0.300000, hs = 150,\n",
      "iteration 0 / 2000: loss 2.303978\n",
      "iteration 500 / 2000: loss 2.303610\n",
      "iteration 1000 / 2000: loss 2.302551\n",
      "iteration 1500 / 2000: loss 2.299326\n",
      "val_acc = 0.196000\n",
      "==============================================================\n",
      "lr = 0.000010, bs = 128, rs = 0.300000, hs = 200,\n",
      "iteration 0 / 2000: loss 2.304391\n",
      "iteration 500 / 2000: loss 2.303942\n",
      "iteration 1000 / 2000: loss 2.303401\n",
      "iteration 1500 / 2000: loss 2.297813\n",
      "val_acc = 0.199000\n",
      "==============================================================\n",
      "lr = 0.000010, bs = 128, rs = 0.450000, hs = 50,\n",
      "iteration 0 / 2000: loss 2.303289\n",
      "iteration 500 / 2000: loss 2.303161\n",
      "iteration 1000 / 2000: loss 2.302697\n",
      "iteration 1500 / 2000: loss 2.301764\n",
      "val_acc = 0.199000\n",
      "==============================================================\n",
      "lr = 0.000010, bs = 128, rs = 0.450000, hs = 100,\n",
      "iteration 0 / 2000: loss 2.303975\n",
      "iteration 500 / 2000: loss 2.303700\n",
      "iteration 1000 / 2000: loss 2.302747\n",
      "iteration 1500 / 2000: loss 2.300599\n",
      "val_acc = 0.225000\n",
      "==============================================================\n",
      "lr = 0.000010, bs = 128, rs = 0.450000, hs = 150,\n",
      "iteration 0 / 2000: loss 2.304669\n",
      "iteration 500 / 2000: loss 2.304129\n",
      "iteration 1000 / 2000: loss 2.302972\n",
      "iteration 1500 / 2000: loss 2.300757\n",
      "val_acc = 0.190000\n",
      "==============================================================\n",
      "lr = 0.000010, bs = 128, rs = 0.450000, hs = 200,\n",
      "iteration 0 / 2000: loss 2.305385\n",
      "iteration 500 / 2000: loss 2.304864\n",
      "iteration 1000 / 2000: loss 2.303764\n",
      "iteration 1500 / 2000: loss 2.299719\n",
      "val_acc = 0.194000\n",
      "==============================================================\n",
      "lr = 0.000010, bs = 128, rs = 0.600000, hs = 50,\n",
      "iteration 0 / 2000: loss 2.303484\n",
      "iteration 500 / 2000: loss 2.303298\n",
      "iteration 1000 / 2000: loss 2.303038\n",
      "iteration 1500 / 2000: loss 2.301695\n",
      "val_acc = 0.200000\n",
      "==============================================================\n",
      "lr = 0.000010, bs = 128, rs = 0.600000, hs = 100,\n",
      "iteration 0 / 2000: loss 2.304465\n",
      "iteration 500 / 2000: loss 2.304168\n",
      "iteration 1000 / 2000: loss 2.303642\n",
      "iteration 1500 / 2000: loss 2.301603\n",
      "val_acc = 0.179000\n",
      "==============================================================\n",
      "lr = 0.000010, bs = 128, rs = 0.600000, hs = 150,\n",
      "iteration 0 / 2000: loss 2.305343\n",
      "iteration 500 / 2000: loss 2.304724\n",
      "iteration 1000 / 2000: loss 2.302760\n",
      "iteration 1500 / 2000: loss 2.299535\n",
      "val_acc = 0.199000\n",
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     "text": [
      "E:\\SummerCourse\\CS231n\\Assignment\\assignment1\\cs231n\\classifiers\\neural_net.py:112: RuntimeWarning: divide by zero encountered in log\n",
      "  correct_logprobs = -np.log(probs[range(N),y])\n",
      "E:\\SummerCourse\\CS231n\\Assignment\\assignment1\\cs231n\\classifiers\\neural_net.py:109: RuntimeWarning: overflow encountered in subtract\n",
      "  scores = scores - np.max(scores,axis=1).reshape(-1,1)\n",
      "E:\\SummerCourse\\CS231n\\Assignment\\assignment1\\cs231n\\classifiers\\neural_net.py:109: RuntimeWarning: invalid value encountered in subtract\n",
      "  scores = scores - np.max(scores,axis=1).reshape(-1,1)\n",
      "E:\\SummerCourse\\CS231n\\Assignment\\assignment1\\cs231n\\classifiers\\neural_net.py:138: RuntimeWarning: invalid value encountered in greater\n",
      "  grad_hidden_in = (hidden_out > 0) * grad_hidden_out\n"
     ]
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    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "best_net = None # store the best model into this \n",
    "best_val = -1\n",
    "#################################################################################\n",
    "# TODO: Tune hyperparameters using the validation set. Store your best trained  #\n",
    "# model in best_net.                                                            #\n",
    "#                                                                               #\n",
    "# To help debug your network, it may help to use visualizations similar to the  #\n",
    "# ones we used above; these visualizations will have significant qualitative    #\n",
    "# differences from the ones we saw above for the poorly tuned network.          #\n",
    "#                                                                               #\n",
    "# Tweaking hyperparameters by hand can be fun, but you might find it useful to  #\n",
    "# write code to sweep through possible combinations of hyperparameters          #\n",
    "# automatically like we did on the previous exercises.                          #\n",
    "#################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "# Here you may change printing show by 500 iters per time in neural_net.py\n",
    "input_size = 32 * 32 * 3\n",
    "num_classes = 10\n",
    "learning_rates = [1e-5, 1e-4, 1e-3, 1e-2]\n",
    "batch_sizes = [128, 256, 512]\n",
    "regularization_strengths = [0.15, 0.30, 0.45, 0.60, 0.75]\n",
    "hidden_sizes = [50, 100, 150, 200]\n",
    "# I use num_iters = 2000 uniformly\n",
    "for lr in learning_rates:\n",
    "    for bs in batch_sizes:\n",
    "        for rs in regularization_strengths:\n",
    "            for hs in hidden_sizes:\n",
    "                myNet = TwoLayerNet(input_size, hs, num_classes)\n",
    "                \n",
    "                # Train the network\n",
    "                print('lr = %f, bs = %d, rs = %f, hs = %d,' % (lr, bs, rs, hs))\n",
    "                status = myNet.train(X_train, y_train, X_val, y_val,\n",
    "                                    learning_rate=lr, reg=rs,\n",
    "                                     num_iters=2000, batch_size=bs,\n",
    "                                    learning_rate_decay=0.95, verbose=True)\n",
    "                val_accuracy = (myNet.predict(X_val) == y_val).mean()\n",
    "                print('val_acc = %f' % (val_accuracy))\n",
    "                print('==============================================================')\n",
    "                if val_accuracy > best_val:\n",
    "                    best_val = val_accuracy\n",
    "                    best_net = myNet\n",
    "                \n",
    "    \n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best validation accuracy is: 0.539\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the weights of the best network\n",
    "print('The best validation accuracy is:',best_val)\n",
    "show_net_weights(best_net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run on the test set\n",
    "When you are done experimenting, you should evaluate your final trained network on the test set; you should get above 48%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy:  0.532\n"
     ]
    }
   ],
   "source": [
    "test_acc = (best_net.predict(X_test) == y_test).mean()\n",
    "print('Test accuracy: ', test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question**\n",
    "\n",
    "Now that you have trained a Neural Network classifier, you may find that your testing accuracy is much lower than the training accuracy. In what ways can we decrease this gap? Select all that apply.\n",
    "\n",
    "1. Train on a larger dataset.\n",
    "2. Add more hidden units.\n",
    "3. Increase the regularization strength.\n",
    "4. None of the above.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$\n",
    "\n",
    "$\\color{blue}{\\textit Your Explanation:}$\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
